{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pymysql\n",
    "plt.rc('font', **{'family':'Microsoft YaHei, SimHei'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>press</th>\n",
       "      <th>org_name</th>\n",
       "      <th>translator</th>\n",
       "      <th>pub_date</th>\n",
       "      <th>pages_num</th>\n",
       "      <th>price</th>\n",
       "      <th>binding</th>\n",
       "      <th>series</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>score</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000019</td>\n",
       "      <td>政治无意识</td>\n",
       "      <td>弗雷德里克.詹姆逊</td>\n",
       "      <td>中国社会科学出版社</td>\n",
       "      <td></td>\n",
       "      <td>王逢振/陈永国</td>\n",
       "      <td>1999-8</td>\n",
       "      <td>297</td>\n",
       "      <td>35.00元</td>\n",
       "      <td>平装</td>\n",
       "      <td>知识分子图书馆</td>\n",
       "      <td>9787500425564</td>\n",
       "      <td>7.5</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000034</td>\n",
       "      <td>生死遗言</td>\n",
       "      <td>伊能静</td>\n",
       "      <td>现代出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2002-10</td>\n",
       "      <td>203</td>\n",
       "      <td>18.00元</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787800288494</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000041</td>\n",
       "      <td>游泳技巧图解</td>\n",
       "      <td>高桥雄介/吉村丰</td>\n",
       "      <td>北京体育大学出版社</td>\n",
       "      <td></td>\n",
       "      <td>边静/等</td>\n",
       "      <td>2001-10-1</td>\n",
       "      <td>223</td>\n",
       "      <td>20.00元</td>\n",
       "      <td>平装(无盘)</td>\n",
       "      <td></td>\n",
       "      <td>9787810514156</td>\n",
       "      <td>8.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000064</td>\n",
       "      <td>炒股必知必读</td>\n",
       "      <td>刘超/周晓烨主编</td>\n",
       "      <td>当代世界出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2000-01</td>\n",
       "      <td></td>\n",
       "      <td>20.00</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787801154194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000065</td>\n",
       "      <td>公关圣经</td>\n",
       "      <td>(美)菲利普.莱斯礼</td>\n",
       "      <td>汕头大学出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2004-03</td>\n",
       "      <td>957</td>\n",
       "      <td>98.00</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787810367004</td>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID   title      author      press org_name translator   pub_date  \\\n",
       "0  1000019   政治无意识   弗雷德里克.詹姆逊  中国社会科学出版社             王逢振/陈永国     1999-8   \n",
       "1  1000034    生死遗言         伊能静      现代出版社                        2002-10   \n",
       "2  1000041  游泳技巧图解    高桥雄介/吉村丰  北京体育大学出版社                边静/等  2001-10-1   \n",
       "3  1000064  炒股必知必读    刘超/周晓烨主编    当代世界出版社                        2000-01   \n",
       "4  1000065    公关圣经  (美)菲利普.莱斯礼    汕头大学出版社                        2004-03   \n",
       "\n",
       "  pages_num   price binding   series           ISBN  score comment_num  \n",
       "0       297  35.00元      平装  知识分子图书馆  9787500425564    7.5         107  \n",
       "1       203  18.00元      平装           9787800288494    7.4        2377  \n",
       "2       223  20.00元  平装(无盘)           9787810514156    8.4          42  \n",
       "3             20.00      平装           9787801154194    0.0           0  \n",
       "4       957   98.00      平装           9787810367004    7.7          44  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = pymysql.connect(host=\"localhost\", user=\"root\", password=\"\", db=\"doubandb\", charset=\"utf8\")\n",
    "sql = 'select * from books'\n",
    "df = pd.read_sql(sql,conn)\n",
    "conn.close()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗（出版年、价格替换为数字，去掉null和空）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>press</th>\n",
       "      <th>org_name</th>\n",
       "      <th>translator</th>\n",
       "      <th>pub_date</th>\n",
       "      <th>pages_num</th>\n",
       "      <th>price</th>\n",
       "      <th>binding</th>\n",
       "      <th>series</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>score</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000019</td>\n",
       "      <td>政治无意识</td>\n",
       "      <td>弗雷德里克.詹姆逊</td>\n",
       "      <td>中国社会科学出版社</td>\n",
       "      <td></td>\n",
       "      <td>王逢振/陈永国</td>\n",
       "      <td>1999</td>\n",
       "      <td>297</td>\n",
       "      <td>35</td>\n",
       "      <td>平装</td>\n",
       "      <td>知识分子图书馆</td>\n",
       "      <td>9787500425564</td>\n",
       "      <td>7.5</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000034</td>\n",
       "      <td>生死遗言</td>\n",
       "      <td>伊能静</td>\n",
       "      <td>现代出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2002</td>\n",
       "      <td>203</td>\n",
       "      <td>18</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787800288494</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000041</td>\n",
       "      <td>游泳技巧图解</td>\n",
       "      <td>高桥雄介/吉村丰</td>\n",
       "      <td>北京体育大学出版社</td>\n",
       "      <td></td>\n",
       "      <td>边静/等</td>\n",
       "      <td>2001</td>\n",
       "      <td>223</td>\n",
       "      <td>20</td>\n",
       "      <td>平装(无盘)</td>\n",
       "      <td></td>\n",
       "      <td>9787810514156</td>\n",
       "      <td>8.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000064</td>\n",
       "      <td>炒股必知必读</td>\n",
       "      <td>刘超/周晓烨主编</td>\n",
       "      <td>当代世界出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2000</td>\n",
       "      <td></td>\n",
       "      <td>20</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787801154194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000065</td>\n",
       "      <td>公关圣经</td>\n",
       "      <td>(美)菲利普.莱斯礼</td>\n",
       "      <td>汕头大学出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2004</td>\n",
       "      <td>957</td>\n",
       "      <td>98</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787810367004</td>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID   title      author      press org_name translator pub_date  \\\n",
       "0  1000019   政治无意识   弗雷德里克.詹姆逊  中国社会科学出版社             王逢振/陈永国     1999   \n",
       "1  1000034    生死遗言         伊能静      现代出版社                         2002   \n",
       "2  1000041  游泳技巧图解    高桥雄介/吉村丰  北京体育大学出版社                边静/等     2001   \n",
       "3  1000064  炒股必知必读    刘超/周晓烨主编    当代世界出版社                         2000   \n",
       "4  1000065    公关圣经  (美)菲利普.莱斯礼    汕头大学出版社                         2004   \n",
       "\n",
       "  pages_num price binding   series           ISBN  score comment_num  \n",
       "0       297    35      平装  知识分子图书馆  9787500425564    7.5         107  \n",
       "1       203    18      平装           9787800288494    7.4        2377  \n",
       "2       223    20  平装(无盘)           9787810514156    8.4          42  \n",
       "3              20      平装           9787801154194    0.0           0  \n",
       "4       957    98      平装           9787810367004    7.7          44  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pub_date'] = df['pub_date'].str.extract(r'([0-9]{4})')\n",
    "# s = df['pub_date'].str[0:4]\n",
    "df['price'] = df['price'].str.extract(r'([0-9]*)')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             56768\n",
       "title          56768\n",
       "author         56768\n",
       "press          56768\n",
       "org_name       56768\n",
       "translator     56768\n",
       "pub_date       55783\n",
       "pages_num      56768\n",
       "price          56768\n",
       "binding        56768\n",
       "series         56768\n",
       "ISBN           56768\n",
       "score          56768\n",
       "comment_num    56768\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             47257\n",
       "title          47257\n",
       "author         47257\n",
       "press          47257\n",
       "org_name       47257\n",
       "translator     47257\n",
       "pub_date       46823\n",
       "pages_num      47257\n",
       "price          47257\n",
       "binding        47257\n",
       "series         47257\n",
       "ISBN           47257\n",
       "score          47257\n",
       "comment_num    47257\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[df['price']!='']\n",
    "df1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID               0\n",
       "title            0\n",
       "author           0\n",
       "press            0\n",
       "org_name         0\n",
       "translator       0\n",
       "pub_date       434\n",
       "pages_num        0\n",
       "price            0\n",
       "binding          0\n",
       "series           0\n",
       "ISBN             0\n",
       "score            0\n",
       "comment_num      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             46823\n",
       "title          46823\n",
       "author         46823\n",
       "press          46823\n",
       "org_name       46823\n",
       "translator     46823\n",
       "pub_date       46823\n",
       "pages_num      46823\n",
       "price          46823\n",
       "binding        46823\n",
       "series         46823\n",
       "ISBN           46823\n",
       "score          46823\n",
       "comment_num    46823\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.dropna()\n",
    "df2.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>press</th>\n",
       "      <th>org_name</th>\n",
       "      <th>translator</th>\n",
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       "      <th>pages_num</th>\n",
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       "      <th>binding</th>\n",
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       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000019</td>\n",
       "      <td>政治无意识</td>\n",
       "      <td>弗雷德里克.詹姆逊</td>\n",
       "      <td>中国社会科学出版社</td>\n",
       "      <td></td>\n",
       "      <td>王逢振/陈永国</td>\n",
       "      <td>1999</td>\n",
       "      <td>297</td>\n",
       "      <td>35</td>\n",
       "      <td>平装</td>\n",
       "      <td>知识分子图书馆</td>\n",
       "      <td>9787500425564</td>\n",
       "      <td>7.5</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000034</td>\n",
       "      <td>生死遗言</td>\n",
       "      <td>伊能静</td>\n",
       "      <td>现代出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2002</td>\n",
       "      <td>203</td>\n",
       "      <td>18</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787800288494</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000041</td>\n",
       "      <td>游泳技巧图解</td>\n",
       "      <td>高桥雄介/吉村丰</td>\n",
       "      <td>北京体育大学出版社</td>\n",
       "      <td></td>\n",
       "      <td>边静/等</td>\n",
       "      <td>2001</td>\n",
       "      <td>223</td>\n",
       "      <td>20</td>\n",
       "      <td>平装(无盘)</td>\n",
       "      <td></td>\n",
       "      <td>9787810514156</td>\n",
       "      <td>8.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000064</td>\n",
       "      <td>炒股必知必读</td>\n",
       "      <td>刘超/周晓烨主编</td>\n",
       "      <td>当代世界出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2000</td>\n",
       "      <td></td>\n",
       "      <td>20</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787801154194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000065</td>\n",
       "      <td>公关圣经</td>\n",
       "      <td>(美)菲利普.莱斯礼</td>\n",
       "      <td>汕头大学出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2004</td>\n",
       "      <td>957</td>\n",
       "      <td>98</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787810367004</td>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID   title      author      press org_name translator pub_date  \\\n",
       "0  1000019   政治无意识   弗雷德里克.詹姆逊  中国社会科学出版社             王逢振/陈永国     1999   \n",
       "1  1000034    生死遗言         伊能静      现代出版社                         2002   \n",
       "2  1000041  游泳技巧图解    高桥雄介/吉村丰  北京体育大学出版社                边静/等     2001   \n",
       "3  1000064  炒股必知必读    刘超/周晓烨主编    当代世界出版社                         2000   \n",
       "4  1000065    公关圣经  (美)菲利普.莱斯礼    汕头大学出版社                         2004   \n",
       "\n",
       "  pages_num price binding   series           ISBN  score comment_num  \n",
       "0       297    35      平装  知识分子图书馆  9787500425564    7.5         107  \n",
       "1       203    18      平装           9787800288494    7.4        2377  \n",
       "2       223    20  平装(无盘)           9787810514156    8.4          42  \n",
       "3              20      平装           9787801154194    0.0           0  \n",
       "4       957    98      平装           9787810367004    7.7          44  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 清洗出版年异常数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    46823.000000\n",
       "mean      2008.227773\n",
       "std         36.999522\n",
       "min       1882.000000\n",
       "25%       2005.000000\n",
       "50%       2010.000000\n",
       "75%       2013.000000\n",
       "max       9789.000000\n",
       "Name: pub_date, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df2.pub_date).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID\n",
       "pub_date    \n",
       "1882       1\n",
       "1899       2\n",
       "1900      69\n",
       "1901       1\n",
       "1905       1"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2[df2['pub_date']<='2013']\n",
    "df3 = df2[['ID']].groupby(by=df2.pub_date).count()\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 出版数量与年份关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6.5))\n",
    "\n",
    "x = pd.to_datetime(df3.index)\n",
    "y = df3['ID']\n",
    "plt.plot(x,y)\n",
    "plt.title('数量与年份的关系')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评分与出版年份关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>9.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>6.410145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>8.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             score\n",
       "pub_date          \n",
       "1882      9.200000\n",
       "1899      7.500000\n",
       "1900      6.410145\n",
       "1901      0.000000\n",
       "1905      8.300000"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df2[['score']].groupby(by=df2['pub_date']).mean()\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6.5))\n",
    "\n",
    "x1 = pd.to_datetime(df4.index)\n",
    "y1 = df4['score']\n",
    "plt.plot(x1,y1)\n",
    "plt.title('评分与年份的关系')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 图书价格分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     35901.000000\n",
       "mean        105.885463\n",
       "std        2906.280962\n",
       "min           0.000000\n",
       "25%          22.000000\n",
       "50%          29.000000\n",
       "75%          46.000000\n",
       "max      360000.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = pd.to_numeric(df2.price)\n",
    "price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2399"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price[price>100].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "price[price>100] = 110"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(price, bins=11, rwidth=0.8, range=(0, 110))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 出版社出版数量TOP20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df2[df2['press']!='']\n",
    "press = df5[['ID']].groupby(by=df5.press).count()\n",
    "top_press = press.sort_values(by='ID',ascending=False)[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_press.plot(kind='bar', figsize=(20, 6.5), legend=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>press</th>\n",
       "      <th>org_name</th>\n",
       "      <th>translator</th>\n",
       "      <th>pub_date</th>\n",
       "      <th>pages_num</th>\n",
       "      <th>price</th>\n",
       "      <th>binding</th>\n",
       "      <th>series</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>score</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000019</td>\n",
       "      <td>政治无意识</td>\n",
       "      <td>弗雷德里克.詹姆逊</td>\n",
       "      <td>中国社会科学出版社</td>\n",
       "      <td></td>\n",
       "      <td>王逢振/陈永国</td>\n",
       "      <td>1999</td>\n",
       "      <td>297</td>\n",
       "      <td>35</td>\n",
       "      <td>平装</td>\n",
       "      <td>知识分子图书馆</td>\n",
       "      <td>9787500425564</td>\n",
       "      <td>7.5</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000034</td>\n",
       "      <td>生死遗言</td>\n",
       "      <td>伊能静</td>\n",
       "      <td>现代出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2002</td>\n",
       "      <td>203</td>\n",
       "      <td>18</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787800288494</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000041</td>\n",
       "      <td>游泳技巧图解</td>\n",
       "      <td>高桥雄介/吉村丰</td>\n",
       "      <td>北京体育大学出版社</td>\n",
       "      <td></td>\n",
       "      <td>边静/等</td>\n",
       "      <td>2001</td>\n",
       "      <td>223</td>\n",
       "      <td>20</td>\n",
       "      <td>平装(无盘)</td>\n",
       "      <td></td>\n",
       "      <td>9787810514156</td>\n",
       "      <td>8.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000064</td>\n",
       "      <td>炒股必知必读</td>\n",
       "      <td>刘超/周晓烨主编</td>\n",
       "      <td>当代世界出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2000</td>\n",
       "      <td></td>\n",
       "      <td>20</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787801154194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000065</td>\n",
       "      <td>公关圣经</td>\n",
       "      <td>(美)菲利普.莱斯礼</td>\n",
       "      <td>汕头大学出版社</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2004</td>\n",
       "      <td>957</td>\n",
       "      <td>98</td>\n",
       "      <td>平装</td>\n",
       "      <td></td>\n",
       "      <td>9787810367004</td>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID   title      author      press org_name translator pub_date  \\\n",
       "0  1000019   政治无意识   弗雷德里克.詹姆逊  中国社会科学出版社             王逢振/陈永国     1999   \n",
       "1  1000034    生死遗言         伊能静      现代出版社                         2002   \n",
       "2  1000041  游泳技巧图解    高桥雄介/吉村丰  北京体育大学出版社                边静/等     2001   \n",
       "3  1000064  炒股必知必读    刘超/周晓烨主编    当代世界出版社                         2000   \n",
       "4  1000065    公关圣经  (美)菲利普.莱斯礼    汕头大学出版社                         2004   \n",
       "\n",
       "  pages_num price binding   series           ISBN  score comment_num  \n",
       "0       297    35      平装  知识分子图书馆  9787500425564    7.5         107  \n",
       "1       203    18      平装           9787800288494    7.4        2377  \n",
       "2       223    20  平装(无盘)           9787810514156    8.4          42  \n",
       "3              20      平装           9787801154194    0.0           0  \n",
       "4       957    98      平装           9787810367004    7.7          44  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评分最高的出版社"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>press</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京出版社出版集团,北京十月文艺出版社</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>台湾民生报,作家出版社联合出版</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江音像</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>聯經出版事業有限公司</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     score\n",
       "press                     \n",
       "北京出版社出版集团,北京十月文艺出版社   10.0\n",
       "台湾民生报,作家出版社联合出版       10.0\n",
       "浙江音像                  10.0\n",
       "聯經出版事業有限公司            10.0"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df5[['score']].groupby(by=df5.press).mean()\n",
    "score = pd.to_numeric(df6.sort_values(by='score',ascending=False).max())\n",
    "df6[df6['score']==10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 出书最多的作者"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>author</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>鲁迅</th>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID\n",
       "author     \n",
       "鲁迅      187"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = df2[df2['author']!='']\n",
    "author = df7[['ID']].groupby(by=df5.author).count()\n",
    "top_author = author.sort_values(by='ID',ascending=False)[0:1]\n",
    "top_author"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高评分与评分数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.4</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score  comment_num\n",
       "0    7.5          107\n",
       "1    7.4         2377\n",
       "2    8.4           42\n",
       "3    0.0            0\n",
       "4    7.7           44"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = df2[['score', 'comment_num']]\n",
    "comment_num = pd.to_numeric(df8.comment_num)\n",
    "df8.comment_num = comment_num\n",
    "df8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    35901.000000\n",
       "mean         7.499279\n",
       "std          2.205897\n",
       "min          0.000000\n",
       "25%          7.400000\n",
       "50%          8.100000\n",
       "75%          8.600000\n",
       "max         10.000000\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8['score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = df8['score'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.1</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.6</td>\n",
       "      <td>4448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.8</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.0</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    score  comment_num\n",
       "6     9.1          178\n",
       "7     8.6         4448\n",
       "8     9.0           61\n",
       "11    8.8          571\n",
       "13    9.0          783"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8[df8['score']>=s]\n",
    "df9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>comment_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comment_num</th>\n",
       "      <td>-0.029179</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                score  comment_num\n",
       "score        1.000000    -0.029179\n",
       "comment_num -0.029179     1.000000"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df9.corr()\n",
    "sns.heatmap(corr)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
